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Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy

Authors Lin HJ, Qiu XK, Zhang B, Zhang JC

Received 8 March 2018

Accepted for publication 12 July 2018

Published 17 September 2018 Volume 2018:11 Pages 5943—5955


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Geoffrey Pietersz

Hengjun Lin, Xueke Qiu, Bo Zhang, Jichao Zhang

Department of Tumor, Anus and Intestine, Jinhua People’s Hospital, Jinhua, Zhejiang 321000, China

Background: Colorectal cancer is a malignant tumor with high death rate. Chemotherapy, radiotherapy and surgery are the three common treatments of colorectal cancer. For early colorectal cancer patients, postoperative adjuvant chemotherapy can reduce the risk of recurrence. For advanced colorectal cancer patients, palliative chemotherapy can significantly improve the life quality of patients and prolong survival. FOLFOX is one of the mainstream chemotherapies in colorectal cancer, however, its response rate is only about 50%.
Methods: To systematically investigate why some of the colorectal cancer patients have response to FOLFOX therapy while others do not, we searched all publicly available database and combined three gene expression datasets of colorectal cancer patients with FOLFOX therapy. With advanced minimal redundancy maximal relevance and incremental feature selection method, we identified the biomarker genes.
Results: A Support Vector Machine-based classifier was constructed to predict the response of colorectal cancer patients to FOLFOX therapy. Its accuracy, sensitivity and specificity were 0.854, 0.845 and 0.863, respectively.
Conclusion: The biological analysis of representative biomarker genes suggested that apoptosis and inflammation signaling pathways were essential for the response of colorectal cancer patients to FOLFOX chemotherapy.

colorectal cancer, FOLFOX therapy, support vector machine, minimal redundancy maximal relevance, incremental feature selection, chemotherapy response

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